Mutual funds aim to generate returns above market averages. While predicting their future portfolio allocations can bring economic advantages, the task remains challenging and largely unexplored. To fill that gap, this work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task, focusing on predicting novel items in a fund's next portfolio. We create a comprehensive benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task. Our findings reveal that predicting novel items in mutual fund portfolios is inherently more challenging than predicting the entire portfolio or only repeated items. While state-of-the-art NBR models are outperformed by simple heuristics when considering both novel and repeated items together, autoencoder-based approaches demonstrate superior performance in predicting only new items. The insights gained from this study highlight the importance of considering domain-specific characteristics when applying recommender systems to mutual fund portfolio prediction. The performance gap between predicting the entire portfolio or repeated items and predicting novel items underscores the complexity of the NNBR task in this domain and the need for continued research to develop more robust and adaptable models for this critical financial application.

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.

At the center of the growing digital economy is data. Data is to the 21st century what oil was to the 20th century. In every industry, it are the companies that can use data effectively that succeed. And investing is no different. In their search for alpha generating ideas, investment managers are increasingly turning to sources of alternative financial data. But what is alternative data and how does it give fund managers an edge? The returns generated by investors can be classified as either alpha or beta.

Global research firm Gartner predicts that 75% of venture capitalists and private equity investors will use artificial intelligence (AI) to make their investment decisions by 2025. Investing in startups is just as, if not more, risky than investing in the money market. Many companies like Motherbrain and SignalFire are already using data to track down companies that are on the cusp of becoming successful. A person's'gut feel' is often the compass for making decisions. However, when it comes to investing in companies and startups, research and advisory firm Gartner estimates that three-fourths of the venture capitalists (VCs), globally, will be using artificial intelligence (AI) to make their decision by 2025.

DWS's stake in Arabesque showed how asset management is moving towards AI-powered investing. Artificial intelligence, hailed as investing's next frontier, is already widespread in various forms, but its true potential in portfolio management is still far from being fulfilled. In a study on AI and finance for the Alan Turing Institute, Professor Bonnie Buchanan puts AI's "impressive" growth down to declining processing and data-storage costs, and an immense availability of data. But compared to other fields, the quantity of data or the ability to create and collect new investment data is still not sufficient, despite its abundance, according to Michael Neumann, head of AI quant investing at Arabesque AI in London. Financial data also comes with a lot of'noise', and the definition of success or failure can be more nuanced.

In computing, a graph database (GDB) is a database which utilises graph structures for semantic queries with nodes, edges, and properties to represent and store data. The graph related data items in the store to a collection of nodes and edges, where edges are representing the relationships across the nodes. Graph databases are a kind of NoSQL database, built to address the limitations of relational databases. While the graph model clearly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases are the fastest-growing category in all of data management.

Remember Aibo, the computerized dog Sony Corp. started selling in 1999 as the first personal robot? Hiro Mizuno, the chief investment officer of the Government Pension Investment Fund, does. So he asked Sony's computer science lab unit to build him a cyberhound using artificial intelligence to help oversee the external fund managers who manage GPIF's ¥175 trillion ($1.6 trillion) in assets. If the training program succeeds, the software watchdog could catch investors who are straying from their comfort zones, help screen potential portfolio managers based on their previous track records, and even distinguish between luck and skill in generating returns. The project, which Mizuno says is part of his experiments in improving the way money is managed, will run through March, but the Sony team recently issued an interim report.

An artificial intelligence-driven compliance technology company founded by a former fund manager and corporate broker has raised £2.5 million in a recent funding round. FeedStock's latest funding round was led by Praetura Ventures, with Force Over Mass and existing investor Illuminate Financial Management also participating in the round. The company provides AI and natural language processing technologies to help both the buy- and sell-side institutions meet various compliance requirements, as well as commercial goals. Founded in 2015, FeedStock was established by former analyst and fund manager at GAM, Lucas Wurfbain, alongside Charlie Henderson, who previously worked as a research analyst and corporate broker. "With our background in highly regulated businesses, we are seeing enormous appetite for our proprietary technology; not only from businesses required to comply with MiFID II, but also for enterprises that are looking to leverage AI as a core component of their business for efficiency gains and revenue generation," Henderson commented on the recent investment.

The arrival of artificial intelligence (AI) is a silent revolution. It will change all sectors, including asset management. Investors are challenged to grasp the nature of this new reality and how artificial intelligence can participate in creation of added-value for listed companies. AI and machine learning1 are the top priorities for most asset managers worldwide2. In the financial services sector, AI is caricatured as a substitute for human employment.

THE JOB of capital markets is to process information so that savings flow to the best projects and firms. That makes high finance sound simple; in reality it is dynamic and intoxicating. It reflects a changing world. Today's markets, for instance, are grappling with a trade war and low interest rates. But it also reflects changes within finance, which constantly reinvents itself in a perpetual struggle to gain a competitive edge.